Comprehensive transcriptomic analysis identifies novel regulators of lung adenocarcinoma

被引:19
作者
Mokhlesi, Amir [1 ]
Talkhabi, Mahmood [1 ]
机构
[1] Shahid Beheshti Univ, Fac Life Sci & Biotechnol, Dept Anim Sci & Marine Biol, Tehran, Iran
关键词
Lung adenocarcinoma; Protein-protein interaction; Bioinformatics analysis; Transcription factor; miRNA; Metabolite; TYROSINE KINASE INHIBITORS; TUMOR PROGRESSION; CANCER; PATHWAYS; METABOLISM; ESTROGEN; TUMORIGENESIS; METHYLATION; SURVIVIN; MICRORNA;
D O I
10.1007/s12079-020-00565-4
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Lung adenocarcinoma (LA) is a subtype of lung cancer that accounts for about 40% of all lung cancers. Analysis of molecular mechanisms controlling this cancer can help scientists to detect, control and treat LA. Here, a microarray dataset (GSE118370) containing six normal lung (NL) and six LA samples was screened using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG and ChEA were used to analyze DEGs-related gene ontology, pathways and transcription factors (TFs), respectively. The Protein-protein interaction network for DEGs and TFs was constructed by STRING and Cytoscape. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 350 genes were upregulated and 608 genes were downregulated in LA. The upregulated genes or LA-related gens were enriched in biological process and pathways such as extracellular matrix disassembly and p53 signaling pathway, whereas the downregulated genes or NL-related genes were enriched in cell adhesion and cell-surface receptor signaling pathway. ESR1, KIF18B, BIRC5, CHEK1, CCNB1 and AURKA were determined as hub genes for LA. FOXA1 and TFAP2A had the highest number of connectivity in LA-related TFs. hsa-miR-192-5p and hsa-miR-215-5p could target the highest number of LA-related genes. Metabolite analysis showed that Estrone and NADPH were among the top ten metabolites associated with LA-related genes. Taken together, LA-related genes, especially the hub genes, TFs, and metabolites might be used as novel markers for LA, as well as for diagnosis and guiding therapeutic strategies of LA.
引用
收藏
页码:453 / 465
页数:13
相关论文
共 57 条
[41]   GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor [J].
Sean, Davis ;
Meltzer, Paul S. .
BIOINFORMATICS, 2007, 23 (14) :1846-1847
[42]   Cytoscape: A software environment for integrated models of biomolecular interaction networks [J].
Shannon, P ;
Markiel, A ;
Ozier, O ;
Baliga, NS ;
Wang, JT ;
Ramage, D ;
Amin, N ;
Schwikowski, B ;
Ideker, T .
GENOME RESEARCH, 2003, 13 (11) :2498-2504
[43]   Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis [J].
Shi, Ke ;
Li, Na ;
Yang, Meilan ;
Li, Wei .
JOURNAL OF CANCER, 2019, 10 (01) :51-60
[44]   Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis [J].
Shi, Yangfeng ;
Li, Yeping ;
Yan, Chao ;
Su, Hua ;
Ying, Kejing .
ONCOLOGY LETTERS, 2019, 18 (06) :5859-5870
[45]   Molecular pathways and therapeutic targets in lung cancer [J].
Shtivelman, Emma ;
Hensing, Thomas ;
Simon, George R. ;
Dennis, Phillip A. ;
Otterson, Gregory A. ;
Bueno, Raphael ;
Salgia, Ravi .
ONCOTARGET, 2014, 5 (06) :1392-1433
[46]  
Szklarczyk D, 2019, NUCLEIC ACIDS RES, V47, pD607, DOI [10.1093/nar/gky1131, 10.1093/nar/gkac1000]
[47]   GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses [J].
Tang, Zefang ;
Li, Chenwei ;
Kang, Boxi ;
Gao, Ge ;
Li, Cheng ;
Zhang, Zemin .
NUCLEIC ACIDS RESEARCH, 2017, 45 (W1) :W98-W102
[48]   A human protein atlas for normal and cancer tissues based on antibody proteomics [J].
Uhlén, M ;
Björling, E ;
Agaton, C ;
Szigyarto, CA ;
Amini, B ;
Andersen, E ;
Andersson, AC ;
Angelidou, P ;
Asplund, A ;
Asplund, C ;
Berglund, L ;
Bergström, K ;
Brumer, H ;
Cerjan, D ;
Ekström, M ;
Elobeid, A ;
Eriksson, C ;
Fagerberg, L ;
Falk, R ;
Fall, J ;
Forsberg, M ;
Björklund, MG ;
Gumbel, K ;
Halimi, A ;
Hallin, I ;
Hamsten, C ;
Hansson, M ;
Hedhammar, M ;
Hercules, G ;
Kampf, C ;
Larsson, K ;
Linskog, M ;
Lodewyckx, W ;
Lund, J ;
Lundeberg, J ;
Magnusson, K ;
Malm, E ;
Nilsson, P ;
Ödling, J ;
Oksvold, P ;
Olsson, I ;
Öster, E ;
Ottosson, J ;
Paavilainen, L ;
Persson, A ;
Rimini, R ;
Rockberg, J ;
Runeson, M ;
Sivertsson, Å ;
Sköllermo, A .
MOLECULAR & CELLULAR PROTEOMICS, 2005, 4 (12) :1920-1932
[49]  
Weber M, 2019, PROSPECTS COST EFFEC
[50]   HMDB 4.0: the human metabolome database for 2018 [J].
Wishart, David S. ;
Feunang, Yannick Djoumbou ;
Marcu, Ana ;
Guo, An Chi ;
Liang, Kevin ;
Vazquez-Fresno, Rosa ;
Sajed, Tanvir ;
Johnson, Daniel ;
Li, Carin ;
Karu, Naama ;
Sayeeda, Zinat ;
Lo, Elvis ;
Assempour, Nazanin ;
Berjanskii, Mark ;
Singhal, Sandeep ;
Arndt, David ;
Liang, Yonjie ;
Badran, Hasan ;
Grant, Jason ;
Serra-Cayuela, Arnau ;
Liu, Yifeng ;
Mandal, Rupa ;
Neveu, Vanessa ;
Pon, Allison ;
Knox, Craig ;
Wilson, Michael ;
Manach, Claudine ;
Scalbert, Augustin .
NUCLEIC ACIDS RESEARCH, 2018, 46 (D1) :D608-D617